Intelligent voice assistants, such as Apple Siri and Amazon Alexa, are widely
used nowadays. These task-oriented dialog systems require a semantic parsing
module in order to process user utterances and understand the action to be
performed. This semantic parsing component was initially implemented by
rule-based or statistical slot-filling approaches for processing simple
queries; however, the appearance of more complex utterances demanded the
application of shift-reduce parsers or sequence-to-sequence models. While
shift-reduce approaches initially demonstrated to be the best option, recent
efforts on sequence-to-sequence systems pushed them to become the
highest-performing method for that task. In this article, we advance the
research on shift-reduce semantic parsing for task-oriented dialog. In
particular, we implement novel shift-reduce parsers that rely on
Stack-Transformers. These allow to adequately model transition systems on the
cutting-edge Transformer architecture, notably boosting shift-reduce parsing
performance. Additionally, we adapt alternative transition systems from
constituency parsing to task-oriented parsing, and empirically prove that the
in-order algorithm substantially outperforms the commonly-used top-down
strategy. Finally, we extensively test our approach on multiple domains from
the Facebook TOP benchmark, improving over existing shift-reduce parsers and
state-of-the-art sequence-to-sequence models in both high-resource and
low-resource settings.

本文探讨了基于转换系统的移进规约语义分析器，利用 Cutting-edge Transformer 架构和 In-order 算法，结合 Facebook TOP 基准中的多个领域的广泛测试，本方法在高资源和低资源情况下均超过了现有的移进规约解析器和最先进的序列到序列模型。